The field of this invention relates to a wireless communication unit, a predistortion circuit and a method of coefficient estimation therefor. The invention is applicable to, but not limited to, modelling of a predistortion circuit that takes into account signals around a local oscillator signal that may affect a linearity performance.
A primary focus and application of the present invention is the field of radio frequency (RF) power amplifiers capable of use in wireless telecommunication applications. Continuing pressure on the limited spectrum available for radio communication systems is forcing the development of spectrally-efficient linear modulation schemes. Since the envelopes of a number of these linear modulation schemes fluctuate, these result in the average power delivered to the antenna being significantly lower than the maximum power, leading to poor efficiency of the power amplifier. Specifically, in this field, there has been a significant amount of research effort in developing high efficiency topologies capable of providing high performances in the ‘back-off’ (linear) region of the power amplifier.
Linear modulation schemes require linear amplification of the modulated signal in order to minimise undesired out-of-band emissions from spectral re-growth. However, the active devices used within a typical RF amplifying device are inherently non-linear by nature. Only when a small portion of the consumed DC power is transformed into RF power, can the transfer function of the amplifying device be approximated by a straight line, i.e. as in an ideal linear amplifier case. This mode of operation provides a low efficiency of DC to RF power conversion, which is unacceptable for portable (subscriber) wireless communication units. Furthermore, the low efficiency is also recognised as being problematic for the base stations.
Furthermore, the emphasis in portable (subscriber) equipment is to increase battery life. To achieve both linearity and efficiency, so called linearisation techniques are used to improve the linearity of the more efficient amplifier classes, for example class ‘AB’, ‘B’ or ‘C’ amplifiers. A number and variety of linearising techniques exist, which are often used in designing linear transmitters, such as Cartesian Feedback, Feed-forward, and Adaptive Pre-distortion.
Modern RF communication standards (such as wideband code division multiple access (WCDMA), time division synchronised code division multiple access (TDSCDMA), etc.), make use of high data-rate yet relatively low-bandwidth digital modulation schemes at the expense of larger peak-to-average power ratios and consequently, more stringent linearity requirements on analog transmitters. Unfortunately, increased linearity requirements usually translate into higher current consumption.
The advent of deep-submicron CMOS has enabled the use of digital predistortion (DPD) techniques capable of compensating for non-linearity. In particular, DPD has enabled analog circuits and devices operating at low-current, with non-linear bias points to be employed. Such techniques are implemented at the cost of additional digital processing, but overall result in a net reduction on current consumption. The use of passive mixers in state-of-the-art direct-conversion RF transmitters has also helped reduce power consumption, whilst maintaining noise and linearity performance. However this type of mixer generates strong local oscillator (LO) harmonics at its output. The presence of these harmonics is unwanted because they are down-converted back to the LO frequency by non-linearity in the following stage.
Digital baseband predistortion circuits are typically located prior to the amplifier and arranged to compensate for the nonlinearity effects in the amplifier, thereby allowing the amplifier to run closer to its maximum output power whilst maintaining low spectral regrowth. An IEEE transaction paper, titled “A generalised memory polynomial model for digital predistortion of RF power amplifiers” describes a recent improvement to predistortion techniques that include memory effects in the predistortion model, which are essential as the bandwidth increases. In this paper, the general Volterra representation is related to the classical Wiener, Hammerstein, and parallel Wiener structures, using a predistortion model based on memory polynomials.
FIG. 1 illustrates a simplified known digital pre-distortion architecture 100 with quadrature inputs Ii 102 and Qi 104. The quadrature inputs Ii 102 and Qi 104 are respectively input to a frequency up-conversion (often a narrowband mixer) stage, having band-pass filtering (BPF) and amplification (denoted as ‘up-conversion+BPF+non-linearity’ in FIG. 1) 106, which in combination introduce non-linearities into signals passing therethrough. The amplification stage is usually a linear driver followed by a high-power non-linear power amplifier (PA). The non-linear output is then applied to a receiver module (denoted as ‘RX’ in FIG. 1) 108 in order to identify quadrature outputs Io 112 and Qoi 114. As illustrated in the distortion graph 116, the quadrature output Io 112 and Qoi 114 follows a distorted, non-linear (non-straight line) response when compared to the quadrature input Ii 102 and Qi 104. To compensate for this distortion, pre-distortion is introduced typically into the quadrature input Ii 102 and Qi 104 as illustrated in the pre-distortion graph 118. In this manner, the distortion effect of frequency up-conversion stage having band-pass filtering and amplification 106 results in a linear, straight line response output.
This typical circuit uses the BPF to remove the local oscillator (LO) harmonics created in the frequency up-conversion stage in order to minimize their effect on the non-linear amplification device(s). In order to create the appropriate pre-distortion response to ensure that the output is linear, the pre-distortion coefficients are often determined using fitting algorithms, where the I-Q outputs are used as predictor data and the I-Q inputs as response data.
One example model to generate the pre-distortion coefficients is provided below, where an input complex baseband signal Z=I+j·Q, where I is the in-phase signal and Q is the quadrature signal. State-of-the-art memory-less, non-linearity models calculate the pre-distorted complex baseband Z′=I′+j·Q′ using:
                                                                                          Z                  ′                                =                                ⁢                                                                            β                      1                                        ⁢                    Z                                    +                                                            β                      3                                        ⁢                                                                                          Z                                                                    2                                        ⁢                    Z                                    +                                                            β                      5                                        ⁢                                                                                          Z                                                                    4                                        ⁢                    Z                                    +                                                            β                      7                                        ⁢                                                                                          Z                                                                    6                                        ⁢                    Z                    ⁢                                                                                  ⁢                    …                                                                                                                          =                                ⁢                                                                            β                      1                                        ⁡                                          (                                              I                        +                                                  j                          ·                          Q                                                                    )                                                        +                                                            β                      3                                        ⁡                                          (                                                                        I                          3                                                +                                                  I                          ·                                                      Q                            2                                                                          +                                                  j                          ⁢                                                                                                          ⁢                                                      Q                            ·                                                          I                              2                                                                                                      +                                                  j                          ⁢                                                                                                          ⁢                                                      Q                            3                                                                                              )                                                        +                  …                                                                    ⁢                                                      [        1        ]            
or equivalently if the coefficients are realI′=β1I+β3(I3+I·Q2)+Q′=β1Q+β3(Q·I2+Q3)+  [2]
In the above model the polynomial coefficients can be made complex in order to take into account the so called amplitude modulation (AM) to phase modulated (PM) distortion effects. However, it is noteworthy that in either case the coefficient multiplying I3 is the same as the one multiplying I·Q2. This limitation creates a problem in the presence of LO harmonics at the input of the non-linear device stage, as illustrated in FIG. 2.
FIG. 2 illustrates a simplified graph of power spectral density (PSD) versus frequency (F). An LO signal 202 is shown. Driver non-linearity creates spectrum regrowth 203 around the carrier and also creates frequency content and spectral re-growth content at LO harmonics, such as the second harmonic content (2LO) 204 and third harmonic content (3LO) 206 of the LO. State-of-the-art models perfectly model this scenario.
However, in reality, and using hard-switched mixers (that are frequently used now due to their good performance in producing low noise), the mixer output, say from frequency up-conversion stage 106 of FIG. 1, also shows frequency content around LO harmonics, due to the square wave effect of the switching operation causing, in particular large 3rd Order harmonics. For example, odd LO harmonic are particularly high in passive mixers (−9.5 dBc 3rd, −14 dBc 5th, . . . ). Frequency content around 2LO also appears due to mixer non-linearities. Some degree of spectrum regrowth around LO is also unavoidable as illustrated graphically 200 in FIG. 2. As in the previous case, driver non-linearity creates spectrum regrowth 213 around the carrier, but in this case, part of the regrowth is created 214, 216 from energy at the second and third LO harmonics 204, 206 mixing down to the LO 212. State-of-the-art non-linearity models struggle with this scenario. Hence, digital pre-distortion (DPD) techniques struggle to accurately compensate for the non-linear effects of such LO harmonics and spectral re-growth.
Thus, digital predistortion techniques in use today assume that there is only the signal around the LO entering a given non-linearity block. Known DPD schemes may or may not employ modeling of memory effects (which is of importance when dealing with wide modulation-bandwidth signals).